# running libraries to use
library(tidyverse)
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## ✔ ggplot2 3.2.1 ✔ purrr 0.3.3
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(janitor)
##
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
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## chisq.test, fisher.test
library(arcos)
library(scales)
##
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
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## discard
## The following object is masked from 'package:readr':
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## col_factor
library(ggrepel)
library(tidycensus)
library(dplyr)
library(rvest)
## Loading required package: xml2
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## Attaching package: 'rvest'
## The following object is masked from 'package:purrr':
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## pluck
## The following object is masked from 'package:readr':
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## guess_encoding
library(mapview)
# In our last in-class lab, we wrote code to rank zip codes based on the median income in a county and how many pills per person the county received from 2006-2012. At the end of the lab, I ran the data for all of the counties in the country. Right now, I am going to run the data for the individual towns within Anne Arundel County. I've looked at the rankings before, but now I will visualize them.
# This is from lab_O7. I requested my own key from the census:
key <- "uO4EK6I"
census_api_key("366af81ca42273ae67ad0729766f54f041bd300d")
## To install your API key for use in future sessions, run this function with `install = TRUE`.
# The following graf is from lab_07:
# Store a table with the median household income value for each county using the get_acs() function. ACS is short for American Community Survey, one of two main census products. In the table that's loaded in, the :estimate" is the median household income for that county, averaged over 5 years ending in 2006.
county_median_household_income <- get_acs(geography = "county", variables = c("B19013_001"), survey="acs5", year = 2012)
## Getting data from the 2008-2012 5-year ACS
# How did I get the variables? I pulled in a table from the tidycensus package that lists all of the thousands of variables available through the census. Load the table below and view it. Use filters in the R Viewer window to find things you might want to use later. You can also find table and variable numbers at https://data.census.gov/cedsci/.
acs_variables <- load_variables(2012, "acs5", cache = TRUE)
# Filter just for Maryland counties
md_county_median_household_income <- county_median_household_income %>%
filter(str_detect(NAME, ", Maryland"))
arcos_county_pills_per_year <- summarized_county_annual(key = key) %>%
clean_names()
maryland_pills_2012 <- arcos_county_pills_per_year %>%
filter(buyer_state == "MD", year=="2012") %>%
select(buyer_county, year, dosage_unit, countyfips)
maryland_population_2012 <- county_population(key = key) %>%
clean_names() %>%
filter(buyer_state == "MD", year=="2012") %>%
select(county_name, population, countyfips)
maryland_2012 <- maryland_pills_2012 %>%
inner_join(maryland_population_2012, by=("countyfips")) %>%
select(buyer_county, dosage_unit, population, countyfips)
md_2012_pills_per_person <- maryland_2012 %>%
select(buyer_county, dosage_unit, population, countyfips) %>%
mutate(pills_per_person = (dosage_unit / population))
md_county_median_household_income <- md_county_median_household_income %>%
mutate(countyfips = GEOID)
md_2012_household_pills <- md_county_median_household_income %>%
inner_join(md_2012_pills_per_person, by=("countyfips")) %>%
arrange(desc(dosage_unit))
# From this, we learn that Anne Arundel County ranks third in the most pills received in the state of Maryland.
md_2012_household_pills %>%
arrange(desc(estimate))
## # A tibble: 24 x 10
## GEOID NAME variable estimate moe countyfips buyer_county dosage_unit
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 24027 Howa… B19013_… 107821 1590 24027 HOWARD 8187185
## 2 24031 Mont… B19013_… 96985 1290 24031 MONTGOMERY 16492456
## 3 24017 Char… B19013_… 93063 1923 24017 CHARLES 5426630
## 4 24009 Calv… B19013_… 92395 3557 24009 CALVERT 4377640
## 5 24003 Anne… B19013_… 86987 1063 24003 ANNE ARUNDEL 19928983
## 6 24035 Quee… B19013_… 86013 3049 24035 QUEEN ANNES 1551490
## 7 24037 St. … B19013_… 85032 1995 24037 SAINT MARYS 3959000
## 8 24021 Fred… B19013_… 83706 1238 24021 FREDERICK 8439302
## 9 24013 Carr… B19013_… 83155 1624 24013 CARROLL 5818440
## 10 24025 Harf… B19013_… 80441 1249 24025 HARFORD 11416250
## # … with 14 more rows, and 2 more variables: population <int>,
## # pills_per_person <dbl>
# Anne Arundel County is the fifth-richest county in Maryland. Baltimore County, which received the most pills, ranks 12th. The poorest county in Maryland, Allegany County, ranks 12th for the most number of pills received. In Maryland, there isn't a direct correlation between median household income and number of pills received.
md_2012_household_pills <- md_county_median_household_income %>%
inner_join(md_2012_pills_per_person, by=("countyfips")) %>%
arrange(desc(pills_per_person))
# Kent County had the highest pills per person in the state of Maryland. Anne Arundel County is not even in the top 10. Allegany, the poorest county, ranks second. Baltimore County, which had the most pills, ranks 8th.
glimpse(md_county_median_household_income) %>%
arrange(desc(estimate))
## Observations: 24
## Variables: 6
## $ GEOID <chr> "24001", "24003", "24005", "24009", "24011", "24013",…
## $ NAME <chr> "Allegany County, Maryland", "Anne Arundel County, Ma…
## $ variable <chr> "B19013_001", "B19013_001", "B19013_001", "B19013_001…
## $ estimate <dbl> 39087, 86987, 66068, 92395, 60735, 83155, 66025, 9306…
## $ moe <dbl> 1341, 1063, 751, 3557, 1809, 1624, 2513, 1923, 1816, …
## $ countyfips <chr> "24001", "24003", "24005", "24009", "24011", "24013",…
## # A tibble: 24 x 6
## GEOID NAME variable estimate moe countyfips
## <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 24027 Howard County, Maryland B19013_001 107821 1590 24027
## 2 24031 Montgomery County, Maryland B19013_001 96985 1290 24031
## 3 24017 Charles County, Maryland B19013_001 93063 1923 24017
## 4 24009 Calvert County, Maryland B19013_001 92395 3557 24009
## 5 24003 Anne Arundel County, Maryland B19013_001 86987 1063 24003
## 6 24035 Queen Anne's County, Maryland B19013_001 86013 3049 24035
## 7 24037 St. Mary's County, Maryland B19013_001 85032 1995 24037
## 8 24021 Frederick County, Maryland B19013_001 83706 1238 24021
## 9 24013 Carroll County, Maryland B19013_001 83155 1624 24013
## 10 24025 Harford County, Maryland B19013_001 80441 1249 24025
## # … with 14 more rows
# Howard County has the highest medium income in Maryland. Anne Arundel County ranks fifth. This surprised me.
# I want to rank the towns in Anne Arundel County based on dosage_unit.
arundel_pharmacies <- total_pharmacies_county(county="Anne Arundel", state="MD", key = key)
arundel_pharmacies %>%
group_by(buyer_city) %>%
summarise(total_pills = sum(total_dosage_unit)) %>%
arrange(desc(total_pills))
## # A tibble: 24 x 2
## buyer_city total_pills
## <chr> <int>
## 1 GLEN BURNIE 45963914
## 2 ANNAPOLIS 19718367
## 3 PASADENA 17366426
## 4 EDGEWATER 6266672
## 5 SEVERN 6112070
## 6 SEVERNA PARK 5078480
## 7 GAMBRILLS 3803201
## 8 HANOVER 3783815
## 9 CROFTON 3744349
## 10 ARNOLD 3567665
## # … with 14 more rows
# Glen Burnie received the most pills... Where does Glen Burnie fall in median household income?
# Where does Glen Burnie fall in terms of median household income in Anne Arundel County?
aac_raw <- county_raw(county="Anne Arundel", state="MD", key = key)
aac_pharm_tracts <- pharm_tracts(county="Anne Arundel", state="MD", key = key)
aac_pharms <- aac_raw %>%
inner_join(aac_pharm_tracts, by=c("BUYER_DEA_NO")) %>%
select(BUYER_CITY, BUYER_ZIP, DOSAGE_UNIT, TRACTCE) %>%
group_by(BUYER_CITY, BUYER_ZIP, TRACTCE) %>%
summarise()
# I was able to group the city, zip and tract to see how these numbers correspond. Annapolis is the only city that had two zip codes. This will be helpful going forward if try to break down DOSAGE_UNIT by zip code.
# v1 <- load_variables(year='2006', dataset='aac_pharms', cache = FALSE, key=key)
# I was trying to load the variables so I could look up median household income, but I kept getting "open.connection(x, "rb") : HTTP error 404." I downloaded rvest, but it didn't help. I also got a warning message about closing an unused connectin.
test <- get_acs(geography = "tract", variables = "B19013_001",
state = "TX", county = "Tarrant", geometry = TRUE)
## Getting data from the 2013-2017 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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aac_monthly <- summarized_county_monthly(county="Anne Arundel", state="MD", key = key)
aac_monthly %>%
arrange(desc(DOSAGE_UNIT))
## BUYER_COUNTY BUYER_STATE year month count DOSAGE_UNIT
## 1 ANNE ARUNDEL MD 2011 06 4387 1808660
## 2 ANNE ARUNDEL MD 2011 03 4626 1797805
## 3 ANNE ARUNDEL MD 2012 08 4602 1780080
## 4 ANNE ARUNDEL MD 2011 12 4221 1769640
## 5 ANNE ARUNDEL MD 2012 05 4256 1754900
## 6 ANNE ARUNDEL MD 2011 08 4455 1745435
## 7 ANNE ARUNDEL MD 2012 03 4214 1718365
## 8 ANNE ARUNDEL MD 2010 12 4422 1717565
## 9 ANNE ARUNDEL MD 2009 12 4572 1715610
## 10 ANNE ARUNDEL MD 2012 01 4122 1702685
## 11 ANNE ARUNDEL MD 2011 05 4298 1700750
## 12 ANNE ARUNDEL MD 2010 11 4549 1698910
## 13 ANNE ARUNDEL MD 2011 09 4256 1697940
## 14 ANNE ARUNDEL MD 2010 06 4551 1696430
## 15 ANNE ARUNDEL MD 2012 07 4189 1691461
## 16 ANNE ARUNDEL MD 2010 03 4668 1684860
## 17 ANNE ARUNDEL MD 2011 10 4122 1683560
## 18 ANNE ARUNDEL MD 2011 11 4073 1681540
## 19 ANNE ARUNDEL MD 2012 06 4044 1679180
## 20 ANNE ARUNDEL MD 2012 04 3998 1654520
## 21 ANNE ARUNDEL MD 2012 02 4004 1653840
## 22 ANNE ARUNDEL MD 2011 01 4302 1640760
## 23 ANNE ARUNDEL MD 2011 07 3974 1638490
## 24 ANNE ARUNDEL MD 2010 08 4439 1629050
## 25 ANNE ARUNDEL MD 2009 03 4343 1625545
## 26 ANNE ARUNDEL MD 2011 04 4169 1624960
## 27 ANNE ARUNDEL MD 2012 10 4180 1622765
## 28 ANNE ARUNDEL MD 2010 07 4315 1607105
## 29 ANNE ARUNDEL MD 2010 04 4278 1603110
## 30 ANNE ARUNDEL MD 2008 12 4210 1589754
## 31 ANNE ARUNDEL MD 2009 07 4328 1589080
## 32 ANNE ARUNDEL MD 2012 11 4030 1587525
## 33 ANNE ARUNDEL MD 2009 06 4405 1587290
## 34 ANNE ARUNDEL MD 2010 10 4282 1578055
## 35 ANNE ARUNDEL MD 2010 09 4084 1569275
## 36 ANNE ARUNDEL MD 2011 02 3951 1565110
## 37 ANNE ARUNDEL MD 2012 09 3868 1555631
## 38 ANNE ARUNDEL MD 2009 04 4147 1533885
## 39 ANNE ARUNDEL MD 2009 09 4207 1529025
## 40 ANNE ARUNDEL MD 2012 12 3943 1528031
## 41 ANNE ARUNDEL MD 2008 10 4454 1525154
## 42 ANNE ARUNDEL MD 2009 11 4065 1519655
## 43 ANNE ARUNDEL MD 2010 05 4041 1508990
## 44 ANNE ARUNDEL MD 2008 07 4270 1501196
## 45 ANNE ARUNDEL MD 2009 10 4051 1498110
## 46 ANNE ARUNDEL MD 2009 08 4057 1468580
## 47 ANNE ARUNDEL MD 2009 05 4048 1465035
## 48 ANNE ARUNDEL MD 2010 01 3941 1444415
## 49 ANNE ARUNDEL MD 2007 10 4376 1429330
## 50 ANNE ARUNDEL MD 2007 06 4467 1428691
## 51 ANNE ARUNDEL MD 2008 09 4109 1422472
## 52 ANNE ARUNDEL MD 2010 02 3831 1414080
## 53 ANNE ARUNDEL MD 2008 04 4239 1407486
## 54 ANNE ARUNDEL MD 2008 08 3953 1388120
## 55 ANNE ARUNDEL MD 2007 05 4387 1384045
## 56 ANNE ARUNDEL MD 2008 01 4116 1384010
## 57 ANNE ARUNDEL MD 2007 08 4288 1378880
## 58 ANNE ARUNDEL MD 2008 06 4031 1371870
## 59 ANNE ARUNDEL MD 2008 05 4084 1369550
## 60 ANNE ARUNDEL MD 2009 01 3704 1356124
## 61 ANNE ARUNDEL MD 2009 02 3643 1351086
## 62 ANNE ARUNDEL MD 2006 10 4294 1344375
## 63 ANNE ARUNDEL MD 2007 11 4047 1337551
## 64 ANNE ARUNDEL MD 2007 07 4264 1335380
## 65 ANNE ARUNDEL MD 2007 12 4026 1327020
## 66 ANNE ARUNDEL MD 2006 08 4306 1310960
## 67 ANNE ARUNDEL MD 2007 01 4070 1308826
## 68 ANNE ARUNDEL MD 2008 11 3707 1306418
## 69 ANNE ARUNDEL MD 2007 03 4090 1305905
## 70 ANNE ARUNDEL MD 2008 02 3923 1296090
## 71 ANNE ARUNDEL MD 2006 03 4092 1288540
## 72 ANNE ARUNDEL MD 2006 11 4033 1285502
## 73 ANNE ARUNDEL MD 2006 06 4155 1278200
## 74 ANNE ARUNDEL MD 2006 12 3925 1255350
## 75 ANNE ARUNDEL MD 2007 04 3904 1250315
## 76 ANNE ARUNDEL MD 2006 05 4024 1217830
## 77 ANNE ARUNDEL MD 2007 09 3677 1184460
## 78 ANNE ARUNDEL MD 2006 09 3834 1168650
## 79 ANNE ARUNDEL MD 2007 02 3645 1166565
## 80 ANNE ARUNDEL MD 2006 04 3647 1153450
## 81 ANNE ARUNDEL MD 2006 07 3736 1149490
## 82 ANNE ARUNDEL MD 2008 03 3365 1109856
## 83 ANNE ARUNDEL MD 2006 02 3539 1103555
## 84 ANNE ARUNDEL MD 2006 01 2829 820500
# January 2006 was significantly lower than the rest of the range. 2011 and 2012 were the higher income years for pills.
aac_buyer <- combined_buyer_annual(county="Anne Arundel", state="MD", key = key)
aac_buyer %>%
group_by(BUYER_DEA_NO, year) %>%
summarise(DOSAGE_UNIT) %>%
arrange(desc(DOSAGE_UNIT))
## # A tibble: 1,095 x 3
## # Groups: BUYER_DEA_NO [260]
## BUYER_DEA_NO year DOSAGE_UNIT
## <chr> <int> <int>
## 1 BC8361343 2012 1333000
## 2 BC8361343 2011 1055000
## 3 BA7945908 2011 836400
## 4 BC8361343 2010 724300
## 5 BC8361343 2009 707700
## 6 BA7945908 2010 698700
## 7 BW8912594 2011 681900
## 8 AR9810448 2011 664100
## 9 AR9810448 2010 617000
## 10 AR9810448 2012 613340
## # … with 1,085 more rows
# Two years in a row, the same buyer received the highest number of pills, and it was significantly higher than the other numbers.
aac_buyer_addresses <- buyer_addresses(county="Anne Arundel", state="MD", key = key)
# I searched the BUYER_DEA_NO from the previous codeblock into this data set. The buyer is Crain Towers Pharmacy, located in Glen Burnie. It is also the site of a National Spine & Pain Center, so that definitely adds legitimacy, but how much?
aac_buyer_details <- combined_buyer_monthly(county="Anne Arundel", state="MD", year="2006", key = key)
country_pills_2012 <- arcos_county_pills_per_year %>%
filter(year=="2006") %>%
select(buyer_county, buyer_state, year, dosage_unit) %>%
arrange(desc(dosage_unit))
# Where Anne Arundel County ranks in the county by year:
# 2006: 108, with 14376402 pills
# 2007: 112, with 15836968 pills
# 2008: 124, with 16671976 pills
# 2009: 117, with 18239025 pills
# 2010: 124, with 19151845 pills
# 2011: 126, with 20354650 pills
# 2012: 125, with 19928983 pills
# Anne Arundel County ranked the highest in 2006, at 108 with a total 14,376,402 pills received.
aac_buyer %>%
filter(year=="2006") %>%
select(BUYER_DEA_NO, BUYER_COUNTY, BUYER_STATE, year, DOSAGE_UNIT) %>%
arrange(desc(DOSAGE_UNIT))
## BUYER_DEA_NO BUYER_COUNTY BUYER_STATE year DOSAGE_UNIT
## 1 BC8361343 ANNE ARUNDEL MD 2006 486800
## 2 BA8520199 ANNE ARUNDEL MD 2006 478700
## 3 AE1602437 ANNE ARUNDEL MD 2006 466900
## 4 BM7307805 ANNE ARUNDEL MD 2006 376750
## 5 BN6050734 ANNE ARUNDEL MD 2006 371900
## 6 AR9810448 ANNE ARUNDEL MD 2006 366200
## 7 AR8294681 ANNE ARUNDEL MD 2006 351200
## 8 BW9265720 ANNE ARUNDEL MD 2006 339100
## 9 BW8958704 ANNE ARUNDEL MD 2006 320500
## 10 BW5477903 ANNE ARUNDEL MD 2006 313400
## 11 BW8912594 ANNE ARUNDEL MD 2006 305700
## 12 BR3908665 ANNE ARUNDEL MD 2006 305600
## 13 BA7945908 ANNE ARUNDEL MD 2006 287020
## 14 BR3926358 ANNE ARUNDEL MD 2006 283500
## 15 AD2445434 ANNE ARUNDEL MD 2006 283300
## 16 AG6486799 ANNE ARUNDEL MD 2006 273100
## 17 BT9190682 ANNE ARUNDEL MD 2006 248500
## 18 AA1773844 ANNE ARUNDEL MD 2006 245000
## 19 AG9232238 ANNE ARUNDEL MD 2006 238000
## 20 AG2556251 ANNE ARUNDEL MD 2006 235200
## 21 BS7612028 ANNE ARUNDEL MD 2006 218500
## 22 BS3075745 ANNE ARUNDEL MD 2006 215300
## 23 BC4312170 ANNE ARUNDEL MD 2006 214200
## 24 AG2556922 ANNE ARUNDEL MD 2006 211200
## 25 BS6859550 ANNE ARUNDEL MD 2006 211000
## 26 AG2556213 ANNE ARUNDEL MD 2006 203600
## 27 BP1089728 ANNE ARUNDEL MD 2006 195600
## 28 BR2613289 ANNE ARUNDEL MD 2006 189600
## 29 AG6259041 ANNE ARUNDEL MD 2006 182700
## 30 BG0352827 ANNE ARUNDEL MD 2006 179300
## 31 AF3198834 ANNE ARUNDEL MD 2006 175100
## 32 AG1945267 ANNE ARUNDEL MD 2006 168600
## 33 AR2983775 ANNE ARUNDEL MD 2006 167700
## 34 BS2075768 ANNE ARUNDEL MD 2006 157700
## 35 BC7412846 ANNE ARUNDEL MD 2006 152500
## 36 BW5511870 ANNE ARUNDEL MD 2006 143000
## 37 BS0800955 ANNE ARUNDEL MD 2006 140600
## 38 BP0616411 ANNE ARUNDEL MD 2006 139400
## 39 BE8893112 ANNE ARUNDEL MD 2006 135600
## 40 AG1060033 ANNE ARUNDEL MD 2006 134200
## 41 AR8161159 ANNE ARUNDEL MD 2006 132800
## 42 BK6133641 ANNE ARUNDEL MD 2006 130100
## 43 BS6983161 ANNE ARUNDEL MD 2006 128900
## 44 BR0785571 ANNE ARUNDEL MD 2006 123900
## 45 BG8564595 ANNE ARUNDEL MD 2006 122400
## 46 BC8273120 ANNE ARUNDEL MD 2006 120580
## 47 BC9173713 ANNE ARUNDEL MD 2006 114000
## 48 BM6526428 ANNE ARUNDEL MD 2006 105000
## 49 BB8767103 ANNE ARUNDEL MD 2006 102100
## 50 AP3260344 ANNE ARUNDEL MD 2006 100000
## 51 BS7821362 ANNE ARUNDEL MD 2006 99600
## 52 BT6714225 ANNE ARUNDEL MD 2006 98100
## 53 BR6032039 ANNE ARUNDEL MD 2006 96400
## 54 BS6914724 ANNE ARUNDEL MD 2006 96000
## 55 BF7945871 ANNE ARUNDEL MD 2006 92300
## 56 BK2758603 ANNE ARUNDEL MD 2006 88300
## 57 BR4661179 ANNE ARUNDEL MD 2006 88100
## 58 AD2436889 ANNE ARUNDEL MD 2006 87000
## 59 AB2916293 ANNE ARUNDEL MD 2006 86600
## 60 BK4438265 ANNE ARUNDEL MD 2006 85900
## 61 BK8112625 ANNE ARUNDEL MD 2006 82300
## 62 BS8713857 ANNE ARUNDEL MD 2006 82200
## 63 AR7473654 ANNE ARUNDEL MD 2006 81300
## 64 BM6464553 ANNE ARUNDEL MD 2006 77200
## 65 BS4348529 ANNE ARUNDEL MD 2006 76250
## 66 BK1461843 ANNE ARUNDEL MD 2006 75800
## 67 AD2436776 ANNE ARUNDEL MD 2006 75200
## 68 BP7567590 ANNE ARUNDEL MD 2006 72600
## 69 BT4932403 ANNE ARUNDEL MD 2006 72300
## 70 AR5963992 ANNE ARUNDEL MD 2006 71500
## 71 BR6205048 ANNE ARUNDEL MD 2006 71100
## 72 BM5891343 ANNE ARUNDEL MD 2006 67300
## 73 BW5511185 ANNE ARUNDEL MD 2006 67000
## 74 BT6578732 ANNE ARUNDEL MD 2006 66800
## 75 BE8491956 ANNE ARUNDEL MD 2006 64300
## 76 BT4932439 ANNE ARUNDEL MD 2006 64200
## 77 BW6417263 ANNE ARUNDEL MD 2006 60600
## 78 BS9204657 ANNE ARUNDEL MD 2006 56700
## 79 BW5056278 ANNE ARUNDEL MD 2006 56200
## 80 BW7790238 ANNE ARUNDEL MD 2006 55400
## 81 BT2116374 ANNE ARUNDEL MD 2006 54700
## 82 BC7566423 ANNE ARUNDEL MD 2006 51300
## 83 BW2185773 ANNE ARUNDEL MD 2006 51100
## 84 AD4635631 ANNE ARUNDEL MD 2006 51000
## 85 BS9093307 ANNE ARUNDEL MD 2006 46000
## 86 AK6734366 ANNE ARUNDEL MD 2006 45300
## 87 BS7738997 ANNE ARUNDEL MD 2006 43000
## 88 BW9677898 ANNE ARUNDEL MD 2006 41220
## 89 BS7031684 ANNE ARUNDEL MD 2006 37100
## 90 BT7419232 ANNE ARUNDEL MD 2006 36200
## 91 AD2436637 ANNE ARUNDEL MD 2006 30300
## 92 BY2690053 ANNE ARUNDEL MD 2006 25520
## 93 BY9985889 ANNE ARUNDEL MD 2006 25100
## 94 BW6621343 ANNE ARUNDEL MD 2006 25000
## 95 BC2680470 ANNE ARUNDEL MD 2006 22300
## 96 AW6325434 ANNE ARUNDEL MD 2006 20580
## 97 BP8923345 ANNE ARUNDEL MD 2006 20100
## 98 BM9131688 ANNE ARUNDEL MD 2006 16500
## 99 FG0052946 ANNE ARUNDEL MD 2006 14600
## 100 BT3490098 ANNE ARUNDEL MD 2006 11100
## 101 BC9982922 ANNE ARUNDEL MD 2006 9900
## 102 BW6044096 ANNE ARUNDEL MD 2006 9300
## 103 BB9943879 ANNE ARUNDEL MD 2006 7900
## 104 BM5904215 ANNE ARUNDEL MD 2006 7600
## 105 BE8133023 ANNE ARUNDEL MD 2006 7500
## 106 AH2304525 ANNE ARUNDEL MD 2006 6600
## 107 AA8514576 ANNE ARUNDEL MD 2006 6500
## 108 BH6624589 ANNE ARUNDEL MD 2006 5800
## 109 BL1266774 ANNE ARUNDEL MD 2006 5500
## 110 AF1648623 ANNE ARUNDEL MD 2006 5280
## 111 AK2689377 ANNE ARUNDEL MD 2006 4140
## 112 BB1820415 ANNE ARUNDEL MD 2006 4080
## 113 AC1307051 ANNE ARUNDEL MD 2006 3400
## 114 BG0813801 ANNE ARUNDEL MD 2006 2015
## 115 BS7576664 ANNE ARUNDEL MD 2006 1900
## 116 AG3204803 ANNE ARUNDEL MD 2006 1756
## 117 AA2377390 ANNE ARUNDEL MD 2006 1600
## 118 BK9505883 ANNE ARUNDEL MD 2006 1000
## 119 BG2719310 ANNE ARUNDEL MD 2006 900
## 120 BR1183336 ANNE ARUNDEL MD 2006 800
## 121 BG1126499 ANNE ARUNDEL MD 2006 600
## 122 BM0738091 ANNE ARUNDEL MD 2006 600
## 123 AB1533428 ANNE ARUNDEL MD 2006 500
## 124 AG8478453 ANNE ARUNDEL MD 2006 500
## 125 AH1250670 ANNE ARUNDEL MD 2006 500
## 126 AS5258707 ANNE ARUNDEL MD 2006 500
## 127 BC6162414 ANNE ARUNDEL MD 2006 500
## 128 AG2363822 ANNE ARUNDEL MD 2006 400
## 129 AS1501041 ANNE ARUNDEL MD 2006 400
## 130 AW2543898 ANNE ARUNDEL MD 2006 400
## 131 BG0936659 ANNE ARUNDEL MD 2006 400
## 132 BH9357143 ANNE ARUNDEL MD 2006 400
## 133 BP3490391 ANNE ARUNDEL MD 2006 400
## 134 BF3339771 ANNE ARUNDEL MD 2006 300
## 135 BH2518376 ANNE ARUNDEL MD 2006 300
## 136 AC3283099 ANNE ARUNDEL MD 2006 200
## 137 BC0328547 ANNE ARUNDEL MD 2006 200
## 138 BG1927803 ANNE ARUNDEL MD 2006 200
## 139 BS8756946 ANNE ARUNDEL MD 2006 200
## 140 BW5121190 ANNE ARUNDEL MD 2006 200
## 141 AB2636338 ANNE ARUNDEL MD 2006 100
## 142 AF8959249 ANNE ARUNDEL MD 2006 100
## 143 AO9463782 ANNE ARUNDEL MD 2006 100
## 144 AS5984427 ANNE ARUNDEL MD 2006 100
## 145 AV1519430 ANNE ARUNDEL MD 2006 100
## 146 BE9929778 ANNE ARUNDEL MD 2006 100
## 147 BL1524037 ANNE ARUNDEL MD 2006 100
## 148 BM2552405 ANNE ARUNDEL MD 2006 100
## 149 BN0352891 ANNE ARUNDEL MD 2006 100
## 150 BS2833475 ANNE ARUNDEL MD 2006 100
## 151 BB4390364 ANNE ARUNDEL MD 2006 19
## 152 BC5044538 ANNE ARUNDEL MD 2006 19
## 153 BT8573001 ANNE ARUNDEL MD 2006 14
## 154 AL2497332 ANNE ARUNDEL MD 2006 10
## 155 AR7350286 ANNE ARUNDEL MD 2006 10
## 156 BS0538174 ANNE ARUNDEL MD 2006 10
## 157 BT0732847 ANNE ARUNDEL MD 2006 10
## 158 BM7635672 ANNE ARUNDEL MD 2006 6
## 159 BS7417125 ANNE ARUNDEL MD 2006 6
## 160 BM6819075 ANNE ARUNDEL MD 2006 4
## 161 BE3805237 ANNE ARUNDEL MD 2006 3
# The top buyer in 2006 was Crain Towers Pharmacy. It was responsible for about 3.386% of the pills sent to Anne Arundel County in 2006.
country_income_05_09 <- get_acs(geography = "county", variables = c("B19013_001"), year = 2009) %>%
arrange(desc(estimate))
## Getting data from the 2005-2009 5-year ACS
# From 2005-2009, Anne Arundel County ranked 34th in median household income in the country. This dataset has a large margin of error.
country_income_08_12 <- get_acs(geography = "county", variables = c("B19013_001"), year = 2012) %>%
arrange(desc(estimate))
## Getting data from the 2008-2012 5-year ACS
# From 2008-2012, Anne Arundel County moved up in rank to 27th in median household income in the country.
# country_income_08_12 <- get_acs(geography = "tract", variables = c("B19013_001"), survey="acs5", year = 2012) %>%
# arrange(desc(estimate))
# Per a Data Camp tip sheet, I tried setting the geography as 'tract,' since I found those in a previous codeblock. But it said my API call had errors in that there was an unknown/unsupported geography heirarchy. When I tried to write county and tract, it said that I had the same argument twice, both of which returned information. So I'm not sure how to get the tract to return information.
aac_tract_income <- get_acs(geography = "tract", variables = "B19013_001",
state = "MD", county = "Anne Arundel", geometry = TRUE)
## Getting data from the 2013-2017 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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# I was able to use this to get the median household incomes based on tract. However, these tract numbers don't match up with the TRACTCE numbers I previously found.
md_population <- state_population(state="MD", key = key)
# Maryland had its highest ranking of pills received in 2006, which is when the population was lowest in the 2006-2012 time period we are analyzing.
country_income_08_12 <- country_income_08_12 %>%
mutate(countyfips = GEOID)
# country_income_pills <- country_income_08_12 %>%
# inner_join(country_pills_2012, by=("countyfips")) %>%
# arrange(desc(dosage_unit))
# ggplot(country_income_pills) +
# geom_point(aes(dosage_unit, estimate)) +
# labs(title="Dosage Unit by Median Income", caption = "Source: DEA ARCOS database, via Washington Post", fill="buyer_county") +
# scale_y_continuous(labels = comma) +
# scale_x_continuous(labels = comma) +
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
# geom_smooth(aes(dosage_unit, estimate), method = "lm", se = FALSE)
# From this, I can see that the county with the highest dosage unit was under the country's average for median household income.
v17 <- load_variables(2017, "acs5", cache = TRUE)
x <- v17 %>%
filter(str_detect(concept, "EMPLOY"))
# This should tell me the employment status of the labor force in Anne Arundel County in 2012. B23025_002
aac_labor_force <- get_acs(geography = "county", variables = c("B23025_002"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# From the 2008-2012 census data, there were 303,444 people in the workforce.
# This is the breakdown of black people. B02001_003
aac_black <- get_acs(geography = "county", variables = c("B02001_003"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# In Anne Arundel County in 2012, there were 82,542 black people.
# This is the breakdown of asian people. B02001_005
aac_asian <- get_acs(geography = "county", variables = c("B02001_005"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# In 2012, there were 18,618 asian people in Anne Arundel County.
# This will give a breakdown of the white population. B02001_002
aac_white <- get_acs(geography = "county", variables = c("B02001_002"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# In Anne Arundel County in 2012, there were 408,521 white people.
aac_population_2012 <- county_population(state = "MD", county = "Anne Arundel", key = key)
# In 2012, Anne Arundel County had a population of 538,473 people. Based on my three previous code blocks, Anne Arundel County is largely white.
aac_race <- get_acs(geography = "county", variables = c("B02001_001"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# This gives the same number as the county_population I did in the previous code block.
aac_all_races <- aac_white %>%
inner_join(aac_black, by=("GEOID"))
poverty_rate <- get_acs(geography = "county", variables = c("B06012_002"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
race <- get_acs(geography = "county", variables = c("B02001_001"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
pop_poverty <- race %>%
inner_join(poverty_rate, by=c("GEOID")) %>%
select(GEOID, NAME.x, estimate.x, estimate.y) %>%
mutate(poverty_rate = estimate.y/estimate.x *100)
# I tried several times to rename the columns: estimate.x to "population" and estimate.y to "poverty. Neither the rename nor mutate functions worked. I tried colnames, as well. It kept telling me the object wasn't found, those column types weren't supported or spouting random error messages. I'm not sure my dplyr is properly installed.
# With this, I joined the population and poverty tables and created a new column to calculate the poverty rate in every county in the country (except Puerto Rico, which is not included in this dataset).
# mapview(pop_poverty, xcol = "estimate.x", ycol = "estimate.y", legend = TRUE)
# Can I not map this out because the data is by county? And there isn't a specific column for state? Is there an easy fix to this?
white_pop <- aac_white %>%
inner_join(race, by="GEOID") %>%
select(GEOID, NAME.x, estimate.x, estimate.y) %>%
mutate(white_percentage = estimate.x/estimate.y *100)
# With this, I found the number of people who identify as white and then compared that to the county's total population to find the percentage of white residents in each county. Much of the research says the opioid crisis largely impacted the white population, and I want to see if that's true. The next step will be to compare this to the dosage unit.
white_poverty <- pop_poverty %>%
inner_join(white_pop, by="GEOID") %>%
select(GEOID, NAME.x.x, poverty_rate, white_percentage)
ggplot(white_poverty) +
geom_point(aes(poverty_rate, white_percentage)) +
labs(title="HED", caption = "Source: DEA ARCOS database, via Washington Post", fill="") +
scale_y_continuous(labels = comma) +
scale_x_continuous(labels = comma) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
geom_smooth(aes(poverty_rate, white_percentage), method = "lm", se = FALSE)
## Warning: Removed 78 rows containing non-finite values (stat_smooth).
## Warning: Removed 78 rows containing missing values (geom_point).
# This isn't perfect, but from this graph, we can see that the areas with a higher white percentage have a much lower poverty rate. Now I need to add in dosage unit and unemployment.
# I need to find the total population of men in the workforce, then the total number of women in the workforce. Then I will add those together and divide it by the total population, and this will give me a (very) rough idea of what the unemployment rate is.
# The variable I'm using is unemployment based on health care enrollment. I want to see if this works. B27011_008
unemployment_by_healthcare <- get_acs(geography = "county", variables = c("B27011_008"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
total_unemplyment <- unemployment_by_healthcare %>%
inner_join(race, by="GEOID") %>%
select(GEOID, NAME.x, estimate.x, estimate.y) %>%
mutate(unemployment_rate = estimate.x/estimate.y *100)
# These numbers aren't as outrageous as I thought they would be, so I could see this being fairly accurate.
everything <- total_unemplyment %>%
inner_join(white_poverty, by="GEOID") %>%
select(GEOID, NAME.x, unemployment_rate, poverty_rate, white_percentage)
# So far, I don't see too many shocking trends. The places with the highest poverty rate generally have a lower white population. Poverty and unemployment are generally similar. I need to add in dosage unit to get the key piece of my analysis, but I don't know how to get dosage unit for every county.
pills <- summarized_county_annual(key = key)
pills_population <- pills %>%
inner_join(race, by=c("countyfips" = "GEOID")) %>%
group_by(BUYER_COUNTY, BUYER_STATE, countyfips) %>%
summarise(total_pills = sum(DOSAGE_UNIT), total_population = sum(estimate)) %>%
mutate(pills_per_person = total_pills/total_population) %>%
inner_join(everything, by=c("countyfips" = "GEOID"))
county_geodata_shifted <- get_acs(geography = "county",
variables = "B01001_001", geometry = TRUE, shift_geo = TRUE)
## Getting data from the 2013-2017 5-year ACS
## Using feature geometry obtained from the albersusa package
## Please note: Alaska and Hawaii are being shifted and are not to scale.
pills_population <- county_geodata_shifted %>%
inner_join(pills_population, by=c("GEOID" = "countyfips"))
mapview(pills_population, zcol = "pills_per_person", legend = TRUE)
ggplot(pills_population) +
geom_point(aes(total_pills, total_population)) +
labs(title="HED", caption = "Source: DEA ARCOS database, via Washington Post", fill="") +
scale_y_continuous(labels = comma) +
scale_x_continuous(labels = comma) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
geom_smooth(aes(total_pills, total_population), method = "lm", se = FALSE)
# install.packages("corrr")
# install.packages('lsr')
library(lsr)
library(corrr)
##
## Attaching package: 'corrr'
## The following object is masked from 'package:lsr':
##
## correlate
# glimpse(pills_population)
# pills_population %>%
# ungroup() %>%
# select(-BUYER_COUNTY, -BUYER_STATE, -countyfips, -NAME.x) %>%
# correlate()
# The correlate function allows me to quickly see the relationships between each of these factors without creating a ton of scatter plots. There isn't too high of a correlation between anything I'm looking at, but the highest correlations are between total pills and white percentage, and poverty rate and pills per person.